International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Class of Photometric Invariants: Separating Material from Shape and Illumination
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination Normalization with Time-Dependent Intrinsic Images for Video Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Approach for Shadow Extraction from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Recovering Surface Layout from an Image
International Journal of Computer Vision
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
A perception-based color space for illumination-invariant image processing
ACM SIGGRAPH 2008 papers
Entropy Minimization for Shadow Removal
International Journal of Computer Vision
User-assisted intrinsic images
ACM SIGGRAPH Asia 2009 papers
Tricolor attenuation model for shadow detection
IEEE Transactions on Image Processing
A survey of cast shadow detection algorithms
Pattern Recognition Letters
Estimating the Natural Illumination Conditions from a Single Outdoor Image
International Journal of Computer Vision
Exploiting publicly available cartographic resources for aerial image analysis
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Ortho-image analysis for producing lane-level highway maps
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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Detecting shadows from images can significantly improve the performance of several vision tasks such as object detection and tracking. Recent approaches have mainly used illumination invariants which can fail severely when the qualities of the images are not very good, as is the case for most consumer-grade photographs, like those on Google or Flickr. We present a practical algorithm to automatically detect shadows cast by objects onto the ground, from a single consumer photograph. Our key hypothesis is that the types of materials constituting the ground in outdoor scenes is relatively limited, most commonly including asphalt, brick, stone, mud, grass, concrete, etc. As a result, the appearances of shadows on the ground are not as widely varying as general shadows and thus, can be learned from a labelled set of images. Our detector consists of a three-tier process including (a) training a decision tree classifier on a set of shadow sensitive features computed around each image edge, (b) a CRF-based optimization to group detected shadow edges to generate coherent shadow contours, and (c) incorporating any existing classifier that is specifically trained to detect grounds in images. Our results demonstrate good detection accuracy (85%) on several challenging images. Since most objects of interest to vision applications (like pedestrians, vehicles, signs) are attached to the ground, we believe that our detector can find wide applicability.